True random number generator algorithm
In fact, this is what the HotBits people at Fourmilab in Switzerland are doing. The points in time at which a radioactive source decays are completely unpredictable, and can be sampled and fed into a computer, avoiding any buffering mechanisms in the operating system. To the program, it will seem as though the keys were pressed almost simultaneously.Ī better source of entropy is a radioactive source. Keystrokes, for example, are often buffered by the computer's operating system, meaning that several keystrokes are collected before they are sent to the program waiting for them. In practice, however, it can be tricky to use user input as a source of entropy. A source of entropy can be very simple, like the little variations in somebody's mouse movements or in the amount of time between keystrokes. True random numbers are typically generated by sampling and processing a source of entropy outside the computer. For example, WASTE samples user mouse movements to generate randomness:īut even something as seemingly random as user input can be predictable not all environmental sources are suitably random: To have any hope of producing truly random data, you must reach outside the computer and sample the analog world. Computers are great number crunchers, but they're lousy random number generators. Although both of these methods produce reasonable randomness, they're ultimately still pseudo-random number generators. But I don't see any obvious patterns in the resulting data.
#True random number generator algorithm how to
I have no idea how to test for true randomness. Sw.WriteLine(Math.Abs(BitConverter.ToInt32(b, 0))) Using (StreamWriter sw = new StreamWriter("random.csv")) RandomNumberGenerator rng = RandomNumberGenerator.Create() So I wrote the following program, which compares the two random number methods available in the. In fact, a perfect set of encrypted data is indistinguishable from random data. Good cryptography requires high quality random data. An MD4 hash of the user's environment (username, computer name, search path, etc).Various high precision CPU performance counters.It also incorporates the following environmental factors in its calculations: But perhaps sufficiently random for practical purposes is enough. These cannot be random numbers because they're produced by a computer algorithm computers are physically incapable of randomness.
Knuth's subtractive random number generator algorithm, from The Art of Computer Programming, volume 2: Seminumerical Algorithms. The current implementation of the Random class is based on Donald E. The chosen numbers are not completely random because a definite mathematical algorithm is used to select them, but they are sufficiently random for practical purposes. Pseudo-random numbers are chosen with equal probability from a finite set of numbers. NET framework provides two random number generators. Computers are Lousy Random Number Generators